Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration without sharing raw data, it remains vulnerable to anomalous updates and privacy leakage during parameter exchange. These challenges are amplified in open industrial environments, necessitating zero-trust (ZT) mechanisms, where no participant is inherently trusted. To address these challenges, this work proposes ZTFed-MAS2S, a ZT federated learning framework that integrates a multihead attention-based sequence-to-sequence imputation model. ZTFed integrates verifiable differential privacy with noninteractive zero-knowledge proofs and a confidentiality and integrity verification mechanism to ensure verifiable privacy preservation and secure model parameters transmission. A dynamic trust-aware aggregation mechanism is employed, where trust is propagated over similarity graphs to enhance robustness, and communication overhead is reduced via sparsity- and quantization-based compression. MAS2S captures long-term dependencies in wind power data for accurate imputation. Extensive experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation, demonstrating its effectiveness as a secure and efficient solution for practical applications in the energy sector.

With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

Music recommendation methods predict users' music preference primarily based on historical ratings. Meanwhile, manifold personal factors of users are also important for the problem, and research efforts have been made to improve the recommendation performance with auxiliary user information. As an important indicator of users' personal traits and states, the numerous social media content (e.g., texts, images and short videos), however, is still hardly exploited. In this work, we systematically study the utilization of multimodal social media content for music recommendation. We define groups of both targeted handcrafted features and generic deep features for each modality, and further propose an Attentive Multimodal Autoencoder approach (AMAE) to learn cross-modal latent representations from the extracted features. Attention mechanism is also employed to integrate users' global and contextual music preference with alterable weights. Experiments demonstrate remarkable improvement of recommendation performance (+2.40% in Hit Ratio and +3.30% in NDCG), manifesting the effectiveness of our AMAE approach, as well as the significance of incorporating social media content data in music recommendation.

Visual sign language recognition is an interesting and challenging problem. To create a discriminative representation, a hierarchical Grassmann covariance matrix (HGCM) model is proposed for sign description. Furthermore, a multi-temporal belief propagation (MTBP) based segmentation approach is presented for continuous sequence spotting. Concretely speaking, a sign is represented by multiple covariance matrices, followed by evaluating and selecting their most significant singular vectors. These covariance matrices are transformed into a more compact and discriminative HGCM, which is formulated on the Grassmann manifold. Continuous sign sequences can be recognized frame by frame using the HGCM model, before being optimized by MTBP, which is a carefully designed graphic model. The proposed method is thoroughly evaluated on isolated and synthetic and real continuous sign datasets as well as on HDM05. Extensive experimental results convincingly show the effectiveness of our proposed framework.

Video streaming services heavily depend on the video recommender system to help the users discover videos they would enjoy. Most existing recommender systems compute video relevance based on users implicit feedbacks, e.g., watch and search behaviors. For example, one can use Collaborative Filtering based methods to model the user-video preference, and compute the video-video relevance scores. However, when a new coming video is added to the library, the recommender system has to deal with the cold-start problem, i.e., to bootstrap the video relevance score with very few user behavior with respect to the newly added video. To solve this problem, in this paper we propose a content-based video recommendation approach by taking the advantage of deep convolutional neural networks to alleviate the cold-start problem. The proposed approach works well, especially in the case of serious data incompleteness. In addition to the vision feature, we also conduct extensive evaluation on video meta-data, and audio features.

Deaf person has a large social community around the world. The smooth communication is very difficult for these hard of hearings. Automatic Sign Language Recognition (SLR) can build the bridge between the deaf and the hearings and turn the seamless interaction into reality. This paper presents a visualized communication tool for the hard of hearings, i.e. a large vocabulary sign language recognition system based on the RGB-D data input. A novel Grassmann Covariance Matrix (GCM) representation is used to encode a long-term dynamics of a sign sequence and the discriminative kernel SVM is adopted for the sign classification. For continuous sign language recognition, a probability inference method is used to determine the spotting from the labels of sequential frames. Some basic evaluation and comparison of our recognition algorithms are conducted in our collected datasets. This demo will show the recognition of both isolated sign words and the continuous sign language sentences.